The learning goals from out talk at the European summit on Thursday are:
Goal 1: Why is data driven decision making accelerating at pace?
Statistical analysis is focused on deriving findings from data. Information quality is expanding this view by considering the derivation of information from data. The consideration of information quality links decision making to specific goals, thereby accelerating the building of knowledge and data driven decision making.
Goal 2: How do we ensure our data, analytics, interpretations, conclusions are fit for the intended purpose?
Information quality requires a proactive identification of goal and utility. These two information quality components are key to ensure our data, analytics, interpretations and conclusions are fit for the intended purpose.
Goal 3: How do we scale the use of data analytics to ensure our colleagues who may not be formally trained statisticians or data scientists can get the best from their data analytic endeavors?
The trick is to move the discussion from data and statistical analysis to information quality. This is not a statistical topic per se so that the “colleagues” you refer to have equal standing in the argumentation. This is the essence of democratization of statistics, in a wide sense. The approach requires a spirit of teamwork. Another benefit of focusing on information quality.
